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maffei2443 / cd_nse_ml_ref.txt
Created December 7, 2021 01:36 — forked from emrev12/cd_nse_ml_ref.txt
A collection of references for concept drift detection and machine learning in non-stationary environments.
[LFR] : Wang, H., Abraham, Z., 2015. Concept drift detection for stream- ing data. In: International Joint Conference on Neural Networks (IJCNN). IEEE, pp. 1–9.
[DDM] : Gama, J., Medas, P., Castillo, G., Rodrigues, P., 2004. Learning with drift detection. In: Advances in artificial intelligence–SBIA 2004. Springer, pp. 286–295.
[EDDM] : Baena-Garcıa, M., del Campo-A ́vila, J., Fidalgo, R., Bifet, A., Gavalda, R., Morales-Bueno, R., 2006. Early drift detection method. In: Fourth international workshop on knowledge discov- ery from data streams. Vol. 6. pp. 77–86.
[ADWIN] : Bifet, A., Gavalda, R., 2007. Learning from time-changing data with adaptive windowing. In: SDM. Vol. 7. SIAM
[Resampling] : Harel, M., Mannor, S., El-Yaniv, R., Crammer, K., 2014. Concept drift detection through resampling. In: Proceedings of the 31st Inter- national Conference on Machine Learning (ICML-14). pp. 1009– 1017.
[OLINDDA] : Spinosa, E. J., de Leon F de Carvalho, A. P., Gama, J., 2007. Olindda: A cluster-based approach for detec

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maffei2443 / vimeo-download.py
Created December 26, 2017 16:54 — forked from alexeygrigorev/vimeo-download.py
Downloading segmented video from vimeo
import requests
import base64
from tqdm import tqdm
master_json_url = 'https://178skyfiregce-a.akamaihd.net/exp=1474107106~acl=%2F142089577%2F%2A~hmac=0d9becc441fc5385462d53bf59cf019c0184690862f49b414e9a2f1c5bafbe0d/142089577/video/426274424,426274425,426274423,426274422/master.json?base64_init=1'
base_url = master_json_url[:master_json_url.rfind('/', 0, -26) + 1]
resp = requests.get(master_json_url)
content = resp.json()